Cancer-related fatigue—pharmacological interventions: systematic review and network meta-analysis
Bibliographic record
Abstract
INTRODUCTION: Cancer-related fatigue (CRF) is a very common symptom in patients with cancer, and one of the five areas of highest priority in cancer research. There is currently no consensus on pharmacologic interventions for treating CRF. The aim of this systematic review is to provide more clarity on which pharmacologic interventions may be most promising, for future clinical trials. The network meta-analysis provides the ability to compare multiple agents when no direct head-to-head trials of all agents have been performed. METHODS: Medline (PubMed), EMBASE and Cochrane Central Register of Controlled Trials were searched up until 5 March 2021. Studies were included if they reported on a pharmacologic intervention for CRF. Standardised mean differences and corresponding 95% CIs were computed using a random-effects maximum-likelihood model. RESULTS: This review reports on 18 studies and 2604 patients, the most comprehensive review of pharmacologic interventions for CRF at the time of this publication. Methylphenidate, modafinil and paroxetine were superior to placebo. Methylphenidate and modafinil were equivalent to one another. Paroxetine was superior to modafinil. CONCLUSION: Paroxetine should be further studied in future trials. As well, more safety data are needed on pharmacologic interventions.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.016 | 0.043 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.017 | 0.027 |
| Bibliometrics | 0.007 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".